CN112531737A - Wind-light-fuel cell micro-grid frequency control method based on robust firefly-particle swarm hybrid optimization - Google Patents

Abstract

A wind-light-fuel cell micro-grid frequency control method based on robust firefly-particle swarm hybrid optimization comprises the following steps: step 1, modeling a distributed power supply, wherein the process is as follows: 1.1 wind/light/fuel cell/diesel generator model; 1.2 an energy storage system model, wherein extra electric energy generated in off-peak time or when the solar intensity and the wind speed are high can be stored in energy storage equipment such as a flywheel, a battery or a super capacitor, and the stored electric power can be reused in peak load time or wind energy and photovoltaic power generation time; step 2, constructing a micro-grid model for frequency control; and 3, FF-PSO hybrid optimization PID control. The method ensures that the frequency deviation of the microgrid under different operating conditions (such as wind speed change and load demand change) is minimum.

Description

Wind-light-fuel cell micro-grid frequency control method based on robust firefly-particle swarm hybrid optimization

Technical Field

The invention relates to a micro-grid frequency control method.

Background

Power systems typically supply power to different types of loads located at different locations. Due to the inability to obtain such conventional power in remote and isolated locations, due to geographic limitations of installing additional transmission and distribution lines, and the increasing demand for energy, renewable energy sources have been introduced into conventional power systems, such as solar photovoltaic, wind power, micro hydro power, and the like. To meet the increasing demand and to minimize environmental pollution and transmission loss. However, these renewable energy sources have a high degree of uncertainty, and gas power generation is dependent on weather conditions. This phenomenon may cause large fluctuations in the frequency of the power system, threatening the stable operation of the system. Therefore, the patent provides a firefly and particle swarm hybrid optimization technology based on a micro-grid containing a fan, a photovoltaic system, a fuel cell and various energy storage systems for adjusting parameters of a PID controller, so that the frequency deviation of the micro-grid under different operating conditions (such as wind speed change and load demand change) is minimum.

First, most previous studies have proposed some control methods mainly for frequency fluctuations caused by load uncertainty in the power system, but when renewable energy sources such as photovoltaic are popularized, the problems of energy management may be caused. There is research on a small-signal analysis method for a microgrid consisting of a wind power-fuel cell-diesel generator and an energy storage element, but the research does not apply any controller to obtain a better frequency control curve.

Although there is much work on frequency control in literature based on heuristic optimization techniques, they are mainly studied in traditional thermal, hydroelectric systems where the only disturbance is in the form of load variations. However, in current research, the frequency control problem is being studied in micro grids that have intermittent renewable energy sources such as wind and solar photovoltaic. Here, in addition to the variation in load demand, the frequency controller is also affected by wind speed and solar intensity variations. Furthermore, energy management based on wind/photovoltaic/wind power generation/fuel cell/energy storage system microgrid is a crucial issue under multiple input disturbances in the form of load demand, wind speed and solar intensity variations.

Disclosure of Invention

In order to overcome the defects of the prior art, the invention provides a wind-light-fuel cell micro-grid frequency control method based on robust firefly-particle swarm hybrid optimization, so that the frequency deviation of the micro-grid is minimum under different operating conditions (such as wind speed change and load demand change).

The technical scheme adopted by the invention for solving the technical problems is as follows:

a wind-light-fuel cell microgrid frequency control method based on robust firefly-particle swarm hybrid optimization, comprising the following steps of:

step 1, modeling a distributed power supply, wherein the process is as follows:

1.1 wind/light/fuel cell/diesel generator model

Electricity is generated by wind, photovoltaic, fuel cells and diesel generators to meet load demands; wherein 10% -15% of electric power generated by wind energy and photovoltaic is used for generating hydrogen by the water electrolyzer, and then the fuel cell generates electricity according to the load requirement; the diesel generator can be regarded as a standby power supply, and can automatically supply power to a connected load under the condition that other power supplies such as wind power, photovoltaic power and the like are unavailable, the transfer function of the wind power and the photovoltaic power can ignore the nonlinearity and is expressed by a simple first-order linear transfer function:

in the formula, K_WPGAnd K_PVIs a gain constant; t is_WPGAnd T_PVTime constants for wind and photovoltaic, respectively;

and P_PVGOutputting power for each k-th fan in the system; p_WThe mechanical power of the fan;

is the solar irradiance;

the first order transfer function of the water electrolyser, fuel cell and diesel generator is:

in the formula, K_AE，K_FC，K_DEGIs a gain constant; t is_AE、T_FC、T_DEGTime constants of an electrolytic cell, a fuel cell and a diesel generator are respectively set; p_FCOutputting power for the fuel cell; p_AEOutputting power for the electrolytic cell; p_DEGOutputting power for the diesel generator; Δ f is the system frequency deviation;

1.2 energy storage System model

The additional electrical energy generated during off-peak periods or when solar intensity and wind speed are high can be stored in energy storage devices such as flywheels, batteries or super capacitors, the stored electricity can be reused during peak load periods or during periods of wind energy and photovoltaic power generation being insufficient, and the linear transfer function of the flywheel, battery or super capacitor energy storage system is as follows:

in the formula, K_FES，K_BES，K_UCIs the gain constant, T_FES，T_BES，T_UCTime constants of flywheel energy storage/battery energy storage/super capacitor respectively; p_FESStoring energy for the flywheel and outputting power; p_BESOutputting power for the electrical energy storage; p_UCOutputting power for the super capacitor;

step 2, constructing a micro-grid model for frequency control, wherein the process is as follows:

microgrid total power generation P_MGExpressed as:

P_MG＝P_T+P_DEG+P_FCG+P_PVG±P_FES±(P_BESorP_UC) (7)

wherein

P_T＝P_WPG+P_PVG-P_AE (8)

In the formula, P_TThe net power of the fan and the photovoltaic;

the power balance between supply and demand is realized by controlling each power generation unit and energy storage, and is represented as:

ΔP_e＝P_MG-P_L (9)

in the formula, P_MGIs the total power generation of the microgrid; p_LIs the total power demand;

the frequency deviation Δ f is calculated as follows:

in the formula, K_scIs a characteristic constant of the microgrid, at the moment, the transfer function G of the system_sysExpressed as:

wherein M is K_sysAnd D ═ K_sys T_sysRespectively an equivalent inertia constant and a damping constant of the system;

and 3, FF-PSO hybrid optimization PID control, wherein the process is as follows:

carrying out global search by adopting a firefly algorithm, carrying out local search by adopting a particle swarm algorithm, firstly, identifying an effective area of a search space by utilizing the firefly algorithm, and then carrying out next-stage excavation by adopting the particle swarm algorithm; in order to obtain the superiority of the proposed FF-PSO method, the characteristics of the firefly and the particle swarm are coordinated to obtain the best control effect.

Further, the FF-PSO method comprises the steps of:

3.1, initializing a firefly random population, wherein the number of the fireflies, the fluorescence intensity and the iteration number Iter are set;

3.2, randomly initializing the position of each firefly in the target function search range of the ith firefly, and calculating the luminance of the firefly;

3.3, searching the firefly with the maximum light intensity, and updating the position of the firefly;

3.4, judging whether the maximum iteration number is reached or the required precision is reached, if so, carrying out the next step, otherwise, turning to 3.2;

3.5, selecting 10 fireflies with the maximum luminous brightness as the input of the most PSO;

3.6, calculating the fitness of each firefly, comparing the fitness value of each firefly with the individual extreme value, and replacing the individual extreme value with the fitness value if the fitness value is greater than the individual extreme value;

and 3.7, judging whether the maximum iteration number is reached or the required precision is reached, stopping if the maximum iteration number is reached, and otherwise, updating the position and the speed of the firefly and generating a new population.

In the invention, the changes of wind power, photovoltaic power generation and load have uncertainty, and the uncertainty can cause the mismatching between the generated energy and the required quantity in the microgrid to cause the frequency deviation of the power system, thereby threatening the stable operation of the system. To this end, the patent addresses this problem in two ways. Firstly, besides the self-contained fan and photovoltaic of the micro-grid, a fuel cell and various energy storage systems are added on the system structure. Secondly, a firefly and particle swarm hybrid optimization technology is provided for adjusting parameters of the PID controller, so that the frequency deviation of the microgrid under different operating conditions (such as wind speed change and load demand change) is minimum.

The invention has the following beneficial effects: the frequency deviation of the microgrid under different operating conditions (such as wind speed change and load demand change) is minimized.

Drawings

Fig. 1 is a block diagram of a wind/light/fuel cell based microgrid.

Fig. 2 is a microgrid energy management strategy flow diagram.

Fig. 3 is a microgrid linear model.

FIG. 4 is a flow chart of the FF-PSO algorithm.

Detailed Description

The invention is further described below with reference to the accompanying drawings.

Referring to fig. 1 to 3, a wind-light-fuel cell microgrid frequency control method based on robust firefly-particle swarm hybrid optimization comprises the following steps:

step 1, modeling a distributed power supply, wherein the process is as follows:

various distributed power sources are integrated with an energy storage system to improve the quality and reliability of load power supply, however, due to the variation of wind speed and illumination intensity, power generation resources such as wind power and photovoltaic power have randomness and uncertainty, and the characteristic may increase the mismatch degree of active power and reactive power in the system to cause the instability of the system; therefore, the fuel cell is introduced into the system, and the fuel cell is combined with wind energy, photovoltaic energy, energy storage systems such as a battery, a flywheel and a super capacitor to form a hybrid power system with better reliability; linearizing the power supply and energy storage system model, neglecting the nonlinearity, and expressing in a simplified first-order transfer function form to reduce the complexity; although some dynamic information of the system may be lost by simplifying the model, the percentage of load/wind speed changes is small due to the research being small signal analysis, so the above linearization method has small influence on the system behavior and response;

1.1 wind/light/fuel cell/diesel generator model

Electricity is generated by wind, photovoltaic, fuel cells and diesel generators to meet load demands; wherein 10% -15% of the electric power generated by wind energy and photovoltaic is used by a water electrolyzer (AE) to generate hydrogen, and then the fuel cell generates electricity according to the load requirement; the diesel generator can be regarded as a standby power supply, and can automatically supply power to a connected load under the condition that other power supplies such as wind power, photovoltaic power and the like are unavailable, the transfer function of the wind power and the photovoltaic power can ignore the nonlinearity and is expressed by a simple first-order linear transfer function:

in the formula, K_WPGAnd K_PVIs a gain constant; t is_WPGAnd T_PVTime constants for wind and photovoltaic, respectively; p_WPGkAnd P_PVGOutputting power for each k-th fan in the system; p_WThe mechanical power of the fan;

is the solar irradiance;

the water electrolyzer, fuel cell and diesel generator first order transfer functions are:

in the formula, K_AE，K_FC，K_DEGIs a gain constant; t is_AE、T_FC、T_DEGTime constants of an electrolytic cell, a fuel cell and a diesel generator are respectively set; p_FCOutputting power for the fuel cell; p_AEOutputting power for the electrolytic cell; p_DEGOutputting power for the diesel generator; Δ f is the system frequency deviation;

1.2 energy storage System model

The extra electrical energy generated during off-peak hours or when solar intensity and wind speed are high can be stored in energy storage devices such as flywheels, batteries or super capacitors. The stored power can be reused during peak load periods or during periods of wind and photovoltaic power generation unavailability, and the linear transfer function of the flywheel, battery or super capacitor energy storage system is as follows:

in the formula, K_FES，K_BES，K_UCIs the gain constant, T_FES，T_BES，T_UCRespectively the time constants of the flywheel energy storage/battery energy storage super capacitor; p_FESStoring energy for the flywheel and outputting power; p_BESOutputting power for the electrical energy storage; p_UCOutputting power for the super capacitor;

step 2, constructing a micro-grid model for frequency control, wherein the process is as follows:

fig. 1 to 3 show a microgrid formed by connecting each power generation device and an energy storage system. Fig. 1 is a block diagram of a microgrid based on wind/light/fuel cells, fig. 2 is a flowchart of an energy management strategy of the microgrid, fig. 3 is a linear model of the microgrid, and the microgrid combines wind energy, photovoltaics, power resources such as fuel cells and diesel generators with an electrolysis bath, and the electrolysis bath converts part of electric energy generated by the wind energy or photovoltaics into hydrogen as input of the fuel cells; photovoltaic, fuel cell and energy storage systems are typically connected to a load through DC/DC and DC/AC; these converter models are not considered herein in order to simplify the model, and by coordinating the respective advantages and disadvantages of the wind turbine-photovoltaic-fuel cell-diesel generator, the energy management problem is greatly reduced; in addition, in order to improve the reliability of power supply, various energy storage systems are added in the microgrid, so that the shortage of power can be reduced in the peak load demand period; the diesel generator as a backup generator compensates for the power deficit, which demonstrates the autonomous nature of the isolated microgrid configuration.

Microgrid total power generation P_MGExpressed as:

P_MG＝P_T+P_DEG+P_FCG+P_PVG±P_FES±(P_BESorP_UC) (7)

wherein

P_T＝P_WPG+P_PVG-P_AE (8)

In the formula, P_TThe net power of the fan and the photovoltaic.

The power balance between supply and demand is realized by controlling each power generation unit and energy storage, and can be expressed as:

ΔP_e＝P_MG-P_L (9)

in the formula, P_MGIs the total power generation of the microgrid; p_LIs the total power demand.

The frequency deviation Δ f is calculated as follows:

in the formula, K_scIs a characteristic constant of the microgrid. At this time, the transfer function G of the system_sysExpressed as:

wherein M is K_sysAnd D ═ K_sys T_sysThe equivalent inertia constant and the damping constant of the system are respectively. These parameters are taken into account in the modeling, since the heavy rotor of a rotating machine introduces inertia and a load component that varies with frequency.

And 3, FF-PSO hybrid optimization PID control, wherein the process is as follows:

PID controllers are the most versatile and most widely used feedback controllers, favored for their excellent dynamic performance and robust characteristics, and depend on three basic controller parameters, namely proportional gain (Kp), integral gain (Ki) and differential gain (Kd). The gains are optimized and adjusted through a firefly and particle swarm (FF-PSO) hybrid optimization technology so as to reduce the frequency deviation of the system and maintain the stability of the micro-grid;

particle swarm optimization is an evolutionary algorithm technology proposed by Eberhart and Kennedy and designed based on social behaviors of a bird swarm. The particle swarm optimization algorithm is an optimization tool based on a population, each particle updates the position and the speed of the particle according to the experience of the particle and a neighbor, and the particle swarm optimization algorithm is widely welcomed due to the characteristics of simplicity, high convergence speed, easy program reading and the like; similarly, the firefly algorithm is a population-based algorithm, wherein fireflies are characterized in that they produce a flash of light through biochemical processes or bioluminescence, which serves as the primary coupling signal for mating, based on the following three behaviors: (a) all fireflies are hermaphrodite, and regardless of the sex of the other fireflies, they are attracted; (b) the attraction of fireflies is proportional to the luminance of the firefly. Their attractive force is proportional to their light intensity. Thus, for any two twinkling fireflies, the less bright fireflies will move toward the bright one. The luminance is proportional to the distance, and a higher luminance indicates a smaller distance between two fireflies. If the two flickering fireflies are of the same brightness, they will move randomly; (c) the brightness of the firefly is determined by the objective function. In the case of system uncertainty and parameter variations, the particle swarm algorithm or FF algorithm is sometimes problematic for adjusting PID controller parameters.

Therefore, in order to further search an optimal multimodal space and better optimization performance, a mixing method called mixed firefly and particle swarm algorithm is designed to improve the frequency control of the microgrid, the mixed algorithm adopts the firefly algorithm to perform global search, adopts the particle swarm algorithm to perform local search, firstly, the firefly algorithm is used for identifying an effective area of a search space, and then the particle swarm algorithm is used for mining at the next stage; in order to obtain the superiority of the proposed FF-PSO method, the characteristics of the firefly and the particle swarm are coordinated to obtain the best control effect. The specific steps of the FF-PSO algorithm are shown in FIG. 4 and comprise the following steps.

3.1, initializing a firefly random population, wherein the number of the fireflies, the fluorescence intensity and the iteration number Iter are set;

3.2, randomly initializing the position of each firefly in the target function search range of the ith firefly, and calculating the luminance of the firefly;

3.3, searching the firefly with the maximum light intensity, and updating the position of the firefly;

3.4, judging whether the maximum iteration number is reached or the required precision is reached, if so, carrying out the next step, otherwise, turning to 3.2;

3.5, selecting 10 fireflies with the maximum luminous brightness as the input of the most PSO;

3.6, calculating the fitness of each firefly, comparing the fitness value of each firefly with the individual extreme value, and replacing the individual extreme value with the fitness value if the fitness value is greater than the individual extreme value;

and 3.7, judging whether the maximum iteration number is reached or the required precision is reached, stopping if the maximum iteration number is reached, and otherwise, updating the position and the speed of the firefly and generating a new population.

The embodiments described in this specification are merely illustrative of implementations of the inventive concepts, which are intended for purposes of illustration only. The scope of the present invention should not be construed as being limited to the particular forms set forth in the examples, but rather as being defined by the claims and the equivalents thereof which can occur to those skilled in the art upon consideration of the present inventive concept.

Claims (2)

1. A wind-light-fuel cell micro-grid frequency control method based on robust firefly-particle swarm hybrid optimization is characterized by comprising the following steps of:

step 1, modeling a distributed power supply, wherein the process is as follows:

1.1 wind/light/fuel cell/diesel generator model

Electricity is generated by wind, photovoltaic, fuel cells and diesel generators to meet load demands; wherein 10% -15% of electric power generated by wind energy and photovoltaic is used for generating hydrogen by the water electrolyzer, and then the fuel cell generates electricity according to the load requirement; the diesel generator can be regarded as a standby power supply, and can automatically supply power to a connected load under the condition that other power supplies such as wind power, photovoltaic power and the like are unavailable, the transfer function of the wind power and the photovoltaic power can ignore the nonlinearity and is expressed by a simple first-order linear transfer function:

in the formula, K_WPGAnd K_PVIs a gain constant; t is_WPGAnd T_PVTime constants for wind and photovoltaic, respectively;

and P_PVGOutputting power for each k-th fan in the system; p_WThe mechanical power of the fan;

is the solar irradiance;

the first order transfer function of the water electrolyser, fuel cell and diesel generator is:

in the formula, K_AE，K_FC，K_DEGIs a gain constant; t is_AE、T_FC、T_DEGTime constants of an electrolytic cell, a fuel cell and a diesel generator are respectively set; p_FCOutputting power for the fuel cell; p_AEOutputting power for the electrolytic cell; p_DEGOutputting power for the diesel generator; Δ f is the system frequency deviation;

1.2 energy storage System model

The additional electrical energy generated during off-peak periods or when solar intensity and wind speed are high can be stored in energy storage devices such as flywheels, batteries or super capacitors, the stored electricity can be reused during peak load periods or during periods of wind energy and photovoltaic power generation being insufficient, and the linear transfer function of the flywheel, battery or super capacitor energy storage system is as follows:

in the formula, K_FES，K_BES，K_UCIs the gain constant, T_FES，T_BES，T_UCTime constants of flywheel energy storage/battery energy storage/super capacitor respectively; p_FESStoring energy for the flywheel and outputting power; p_BESOutputting power for the electrical energy storage; p_UCOutputting power for the super capacitor;

step 2, constructing a micro-grid model for frequency control, wherein the process is as follows:

microgrid total power generation P_MGExpressed as:

P_MG＝P_T+P_DEG+P_FCG+P_PVG±P_FES±(P_BESorP_UC) (7)

wherein

P_T＝P_WPG+P_PVG-P_AE (8)

In the formula, P_TThe net power of the fan and the photovoltaic;

the power balance between supply and demand is realized by controlling each power generation unit and energy storage, and is represented as:

ΔP_e＝P_MG-P_L (9)

in the formula, P_MGIs the total power generation of the microgrid; p_LIs the total power demand;

the frequency deviation Δ f is calculated as follows:

in the formula, K_scIs a characteristic constant of the microgrid, at the moment, the transfer function G of the system_sysExpressed as:

wherein M is K_sysAnd D ═ K_sys T_sysRespectively an equivalent inertia constant and a damping constant of the system;

and 3, FF-PSO hybrid optimization PID control, wherein the process is as follows:

carrying out global search by adopting a firefly algorithm, carrying out local search by adopting a particle swarm algorithm, firstly, identifying an effective area of a search space by utilizing the firefly algorithm, and then carrying out next-stage excavation by adopting the particle swarm algorithm; in order to obtain the superiority of the proposed FF-PSO method, the characteristics of the firefly and the particle swarm are coordinated to obtain the best control effect.

2. The wind-light-fuel cell microgrid frequency control method based on robust firefly-particle swarm hybrid optimization of claim 1, characterized in that said FF-PSO method comprises the following steps:

3.1, initializing a firefly random population, wherein the number of the fireflies, the fluorescence intensity and the iteration number Iter are set;

3.2, randomly initializing the position of each firefly in the target function search range of the ith firefly, and calculating the luminance of the firefly;

3.3, searching the firefly with the maximum light intensity, and updating the position of the firefly;

3.4, judging whether the maximum iteration number is reached or the required precision is reached, if so, carrying out the next step, otherwise, turning to 3.2;

3.5, selecting 10 fireflies with the maximum luminous brightness as the input of the most PSO;

3.6, calculating the fitness of each firefly, comparing the fitness value of each firefly with the individual extreme value, and replacing the individual extreme value with the fitness value if the fitness value is greater than the individual extreme value;

and 3.7, judging whether the maximum iteration number is reached or the required precision is reached, stopping if the maximum iteration number is reached, and otherwise, updating the position and the speed of the firefly and generating a new population.

Images